7,965 research outputs found
Graphical Markov models, unifying results and their interpretation
Graphical Markov models combine conditional independence constraints with
graphical representations of stepwise data generating processes.The models
started to be formulated about 40 years ago and vigorous development is
ongoing. Longitudinal observational studies as well as intervention studies are
best modeled via a subclass called regression graph models and, especially
traceable regressions. Regression graphs include two types of undirected graph
and directed acyclic graphs in ordered sequences of joint responses. Response
components may correspond to discrete or continuous random variables and may
depend exclusively on variables which have been generated earlier. These
aspects are essential when causal hypothesis are the motivation for the
planning of empirical studies.
To turn the graphs into useful tools for tracing developmental pathways and
for predicting structure in alternative models, the generated distributions
have to mimic some properties of joint Gaussian distributions. Here, relevant
results concerning these aspects are spelled out and illustrated by examples.
With regression graph models, it becomes feasible, for the first time, to
derive structural effects of (1) ignoring some of the variables, of (2)
selecting subpopulations via fixed levels of some other variables or of (3)
changing the order in which the variables might get generated. Thus, the most
important future applications of these models will aim at the best possible
integration of knowledge from related studies.Comment: 34 Pages, 11 figures, 1 tabl
Polar Codes with exponentially small error at finite block length
We show that the entire class of polar codes (up to a natural necessary
condition) converge to capacity at block lengths polynomial in the gap to
capacity, while simultaneously achieving failure probabilities that are
exponentially small in the block length (i.e., decoding fails with probability
for codes of length ). Previously this combination
was known only for one specific family within the class of polar codes, whereas
we establish this whenever the polar code exhibits a condition necessary for
any polarization.
Our results adapt and strengthen a local analysis of polar codes due to the
authors with Nakkiran and Rudra [Proc. STOC 2018]. Their analysis related the
time-local behavior of a martingale to its global convergence, and this allowed
them to prove that the broad class of polar codes converge to capacity at
polynomial block lengths. Their analysis easily adapts to show exponentially
small failure probabilities, provided the associated martingale, the ``Arikan
martingale'', exhibits a corresponding strong local effect. The main
contribution of this work is a much stronger local analysis of the Arikan
martingale. This leads to the general result claimed above.
In addition to our general result, we also show, for the first time, polar
codes that achieve failure probability for any
while converging to capacity at block length polynomial in the gap to capacity.
Finally we also show that the ``local'' approach can be combined with any
analysis of failure probability of an arbitrary polar code to get essentially
the same failure probability while achieving block length polynomial in the gap
to capacity.Comment: 17 pages, Appeared in RANDOM'1
Efficiency improvement of the frequency-domain BEM for rapid transient elastodynamic analysis
The frequency-domain fast boundary element method (BEM) combined with the
exponential window technique leads to an efficient yet simple method for
elastodynamic analysis. In this paper, the efficiency of this method is further
enhanced by three strategies. Firstly, we propose to use exponential window
with large damping parameter to improve the conditioning of the BEM matrices.
Secondly, the frequency domain windowing technique is introduced to alleviate
the severe Gibbs oscillations in time-domain responses caused by large damping
parameters. Thirdly, a solution extrapolation scheme is applied to obtain
better initial guesses for solving the sequential linear systems in the
frequency domain. Numerical results of three typical examples with the problem
size up to 0.7 million unknowns clearly show that the first and third
strategies can significantly reduce the computational time. The second strategy
can effectively eliminate the Gibbs oscillations and result in accurate
time-domain responses
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